Briefings in Bioinformatics

(The TQCC of Briefings in Bioinformatics is 15. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-05-01 to 2024-05-01.)
oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data533
NetCoMi: network construction and comparison for microbiome data in R216
Predicting drug–disease associations through layer attention graph convolutional network194
LDBlockShow: a fast and convenient tool for visualizing linkage disequilibrium and haplotype blocks based on variant call format files185
BioGPT: generative pre-trained transformer for biomedical text generation and mining181
Identifying drug–target interactions based on graph convolutional network and deep neural network170
MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm154
CellTalkDB: a manually curated database of ligand–receptor interactions in humans and mice151
Expression profile of immune checkpoint genes and their roles in predicting immunotherapy response147
Next generation sequencing of SARS-CoV-2 genomes: challenges, applications and opportunities144
AntiCP 2.0: an updated model for predicting anticancer peptides139
InstaDock: A single-click graphical user interface for molecular docking-based virtual high-throughput screening132
AlgPred 2.0: an improved method for predicting allergenic proteins and mapping of IgE epitopes129
Multimodal deep learning for biomedical data fusion: a review127
Prognosis and personalized treatment prediction in TP53-mutant hepatocellular carcinoma: an in silico strategy towards precision oncology125
A deep learning method for predicting metabolite–disease associations via graph neural network125
Predicting the potential human lncRNA–miRNA interactions based on graph convolution network with conditional random field115
A review on drug repurposing applicable to COVID-19113
Computational recognition of lncRNA signature of tumor-infiltrating B lymphocytes with potential implications in prognosis and immunotherapy of bladder cancer111
A roadmap for multi-omics data integration using deep learning110
Circular RNAs and complex diseases: from experimental results to computational models109
Application of deep learning methods in biological networks108
DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence107
Exploration of natural compounds with anti-SARS-CoV-2 activityviainhibition of SARS-CoV-2 Mpro106
Deep-belief network for predicting potential miRNA-disease associations105
The miRNA: a small but powerful RNA for COVID-19104
Biological network analysis with deep learning103
Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research101
Utilizing graph machine learning within drug discovery and development96
Network Pharmacology and bioinformatics analyses identify intersection genes of niacin and COVID-19 as potential therapeutic targets95
A transformer architecture based on BERT and 2D convolutional neural network to identify DNA enhancers from sequence information95
Systemic effects of missense mutations on SARS-CoV-2 spike glycoprotein stability and receptor-binding affinity93
Detection algorithms and attentive points of safety signal using spontaneous reporting systems as a clinical data source93
A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data92
SSI–DDI: substructure–substructure interactions for drug–drug interaction prediction91
Venn diagrams in bioinformatics91
A survey on computational models for predicting protein–protein interactions91
An end-to-end heterogeneous graph representation learning-based framework for drug–target interaction prediction90
M6A2Target: a comprehensive database for targets of m6A writers, erasers and readers88
Molecular characterization, biological function, tumor microenvironment association and clinical significance of m6A regulators in lung adenocarcinoma87
Drug repositioning based on the heterogeneous information fusion graph convolutional network87
Hiplot: a comprehensive and easy-to-use web service for boosting publication-ready biomedical data visualization86
Meta-i6mA: an interspecies predictor for identifying DNAN6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework84
StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides84
Computer-aided prediction and design of IL-6 inducing peptides: IL-6 plays a crucial role in COVID-1983
GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels82
Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions82
Current challenges for unseen-epitope TCR interaction prediction and a new perspective derived from image classification81
ggmsa: a visual exploration tool for multiple sequence alignment and associated data80
Deep-Kcr: accurate detection of lysine crotonylation sites using deep learning method80
Anticancer peptides prediction with deep representation learning features79
DeepTorrent: a deep learning-based approach for predicting DNA N4-methylcytosine sites78
Machine learning revealed stemness features and a novel stemness-based classification with appealing implications in discriminating the prognosis, immunotherapy and temozolomide responses of 906 gliob78
Tumor immune microenvironment lncRNAs77
A weighted bilinear neural collaborative filtering approach for drug repositioning76
POSREG: proteomic signature discovered by simultaneously optimizing its reproducibility and generalizability74
Discovery of G-quadruplex-forming sequences in SARS-CoV-274
Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets74
Multi-view Multichannel Attention Graph Convolutional Network for miRNA–disease association prediction73
Virtual screening and molecular dynamics simulation study of plant-derived compounds to identify potential inhibitors of main protease from SARS-CoV-272
Pan-cancer analysis of NLRP3 inflammasome with potential implications in prognosis and immunotherapy in human cancer71
PharmKG: a dedicated knowledge graph benchmark for bomedical data mining71
Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework70
Graph representation learning in bioinformatics: trends, methods and applications70
Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-1969
Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-1969
Text mining approaches for dealing with the rapidly expanding literature on COVID-1968
Health informatics and EHR to support clinical research in the COVID-19 pandemic: an overview67
Semantic similarity and machine learning with ontologies66
DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations66
Molecular design in drug discovery: a comprehensive review of deep generative models65
Network-based modeling of herb combinations in traditional Chinese medicine65
MetaFS: Performance assessment of biomarker discovery in metaproteomics65
Clinical significance and immunogenomic landscape analyses of the immune cell signature based prognostic model for patients with breast cancer65
Interpretation of deep learning in genomics and epigenomics64
An in silico approach to identification, categorization and prediction of nucleic acid binding proteins63
A graph auto-encoder model for miRNA-disease associations prediction63
MG-BERT: leveraging unsupervised atomic representation learning for molecular property prediction62
Recent advances in biomedical literature mining62
Identification of miRNA–disease associations via deep forest ensemble learning based on autoencoder62
A survey on deep learning in DNA/RNA motif mining62
DeepYY1: a deep learning approach to identify YY1-mediated chromatin loops61
Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides60
DeepDTAF: a deep learning method to predict protein–ligand binding affinity60
Identifying the natural polyphenol catechin as a multi-targeted agent against SARS-CoV-2 for the plausible therapy of COVID-19: an integrated computational approach60
An approach for normalization and quality control for NanoString RNA expression data59
Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset59
Benchmark of filter methods for feature selection in high-dimensional gene expression survival data59
ToxinPred2: an improved method for predicting toxicity of proteins59
Network-based identification genetic effect of SARS-CoV-2 infections to Idiopathic pulmonary fibrosis (IPF) patients59
HINGRL: predicting drug–disease associations with graph representation learning on heterogeneous information networks58
An effective self-supervised framework for learning expressive molecular global representations to drug discovery58
MDF-SA-DDI: predicting drug–drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism57
Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease patients57
Computational prediction and interpretation of both general and specific types of promoters in Escherichia coli by exploiting a stacked ensemble-learning framework57
Deep learning methods for biomedical named entity recognition: a survey and qualitative comparison56
ITP-Pred: an interpretable method for predicting, therapeutic peptides with fused features low-dimension representation55
A protocol for dynamic model calibration55
Pharmacoinformatics and molecular dynamics simulation-based phytochemical screening of neem plant (Azadiractha indica) against human cancer by targeting MCM7 protein55
NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning55
Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data54
A deep learning method for repurposing antiviral drugs against new viruses via multi-view nonnegative matrix factorization and its application to SARS-CoV-254
A molecular modelling approach for identifying antiviral selenium-containing heterocyclic compounds that inhibit the main protease of SARS-CoV-2: an in silico investigation54
Predicting metabolite–disease associations based on auto-encoder and non-negative matrix factorization54
Learning spatial structures of proteins improves protein–protein interaction prediction53
Deep drug-target binding affinity prediction with multiple attention blocks53
Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design53
Deep learning meets metabolomics: a methodological perspective53
m6A regulator-mediated methylation modification patterns and characteristics of immunity and stemness in low-grade glioma53
Computational drug repositioning based on multi-similarities bilinear matrix factorization53
Therapeutic targets and signaling mechanisms of vitamin C activity against sepsis: a bioinformatics study53
GAERF: predicting lncRNA-disease associations by graph auto-encoder and random forest53
DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method51
Prediction and collection of protein–metabolite interactions51
AlphaFold2-aware protein–DNA binding site prediction using graph transformer51
A novel antibacterial peptide recognition algorithm based on BERT51
Comparative analysis of molecular fingerprints in prediction of drug combination effects51
Pharmacometabonomics: data processing and statistical analysis50
Exploring associations of non-coding RNAs in human diseases via three-matrix factorization with hypergraph-regular terms on center kernel alignment50
DeepImmuno: deep learning-empowered prediction and generation of immunogenic peptides for T-cell immunity50
Integrative pharmacological mechanism of vitamin C combined with glycyrrhizic acid against COVID-19: findings of bioinformatics analyses49
Improving cancer driver gene identification using multi-task learning on graph convolutional network49
Deep4mC: systematic assessment and computational prediction for DNA N4-methylcytosine sites by deep learning49
Cell–cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data resources and computational strategies49
A heterogeneous network embedding framework for predicting similarity-based drug-target interactions49
Artificial intelligence in drug discovery: applications and techniques49
Deep-DRM: a computational method for identifying disease-related metabolites based on graph deep learning approaches49
Using deep neural networks and biological subwords to detect protein S-sulfenylation sites49
FoldRec-C2C: protein fold recognition by combining cluster-to-cluster model and protein similarity network49
Large-scale benchmark study of survival prediction methods using multi-omics data48
Machine learning approach to gene essentiality prediction: a review48
Machine learning methods, databases and tools for drug combination prediction48
Machine learning meets omics: applications and perspectives48
Ferroptosis-related lncRNA pairs to predict the clinical outcome and molecular characteristics of pancreatic ductal adenocarcinoma47
Improving protein–ligand docking and screening accuracies by incorporating a scoring function correction term47
CAMOIP: a web server for comprehensive analysis on multi-omics of immunotherapy in pan-cancer47
DeepIPs: comprehensive assessment and computational identification of phosphorylation sites of SARS-CoV-2 infection using a deep learning-based approach47
iCircRBP-DHN: identification of circRNA-RBP interaction sites using deep hierarchical network47
Predicting potential small molecule–miRNA associations based on bounded nuclear norm regularization47
HVIDB: a comprehensive database for human–virus protein–protein interactions47
Machine learning-based tumor-infiltrating immune cell-associated lncRNAs for predicting prognosis and immunotherapy response in patients with glioblastoma47
A review of digital cytometry methods: estimating the relative abundance of cell types in a bulk of cells46
MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph46
ATSE: a peptide toxicity predictor by exploiting structural and evolutionary information based on graph neural network and attention mechanism46
Attention-based Knowledge Graph Representation Learning for Predicting Drug-drug Interactions46
Deep-ABPpred: identifying antibacterial peptides in protein sequences using bidirectional LSTM with word2vec46
Accurate prediction of inter-protein residue–residue contacts for homo-oligomeric protein complexes46
Enriching contextualized language model from knowledge graph for biomedical information extraction46
Transcriptional landscape of cholangiocarcinoma revealed by weighted gene coexpression network analysis45
Tensor decomposition with relational constraints for predicting multiple types of microRNA-disease associations45
Comparative studies of AlphaFold, RoseTTAFold and Modeller: a case study involving the use of G-protein-coupled receptors45
FitDock: protein–ligand docking by template fitting45
DLpTCR: an ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor44
Epidemiological data analysis of viral quasispecies in the next-generation sequencing era44
Evaluating the state of the art in missing data imputation for clinical data44
Deep learning in retrosynthesis planning: datasets, models and tools44
Multi-omics approaches for revealing the complexity of cardiovascular disease43
Unsupervised and self-supervised deep learning approaches for biomedical text mining43
DeepATT: a hybrid category attention neural network for identifying functional effects of DNA sequences43
Benchmarking variant callers in next-generation and third-generation sequencing analysis43
Drug–drug interaction prediction with learnable size-adaptive molecular substructures43
A simple guide to de novo transcriptome assembly and annotation43
Accurate protein function prediction via graph attention networks with predicted structure information43
ENNAVIA is a novel method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides43
Recent advances in network-based methods for disease gene prediction43
LSTM-PHV: prediction of human-virus protein–protein interactions by LSTM with word2vec43
STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction42
SC-MEB: spatial clustering with hidden Markov random field using empirical Bayes42
FusionDTA: attention-based feature polymerizer and knowledge distillation for drug-target binding affinity prediction42
A comprehensive overview and critical evaluation of gene regulatory network inference technologies42
Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification42
Updated review of advances in microRNAs and complex diseases: taxonomy, trends and challenges of computational models41
Predicting human microbe–disease associations via graph attention networks with inductive matrix completion41
Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding41
Accurate and fast cell marker gene identification with COSG41
NPI-GNN: Predicting ncRNA–protein interactions with deep graph neural networks41
Computational identification of eukaryotic promoters based on cascaded deep capsule neural networks41
Transcriptome analysis of cepharanthine against a SARS-CoV-2-related coronavirus40
A review of biomedical datasets relating to drug discovery: a knowledge graph perspective40
DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy40
Attentional multi-level representation encoding based on convolutional and variance autoencoders for lncRNA–disease association prediction40
Identification of drug–target interactions via multiple kernel-based triple collaborative matrix factorization39
Recent advances in user-friendly computational tools to engineer protein function39
AMDE: a novel attention-mechanism-based multidimensional feature encoder for drug–drug interaction prediction39
iAMP-CA2L: a new CNN-BiLSTM-SVM classifier based on cellular automata image for identifying antimicrobial peptides and their functional types39
Porpoise: a new approach for accurate prediction of RNA pseudouridine sites39
Protein design via deep learning38
Computational resources for identifying and describing proteins driving liquid–liquid phase separation38
Proper imputation of missing values in proteomics datasets for differential expression analysis38
ConSIG: consistent discovery of molecular signature from OMIC data38
Drug–drug interaction prediction with Wasserstein Adversarial Autoencoder-based knowledge graph embeddings38
Integrated unsupervised–supervised modeling and prediction of protein–peptide affinities at structural level38
Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes37
A comprehensive survey on computational methods of non-coding RNA and disease association prediction37
Anthem: a user customised tool for fast and accurate prediction of binding between peptides and HLA class I molecules37
Drug–target interaction predication via multi-channel graph neural networks37
Protein–RNA interaction prediction with deep learning: structure matters37
Identification and characterization of circRNAs encoded by MERS-CoV, SARS-CoV-1 and SARS-CoV-237
A network embedding framework based on integrating multiplex network for drug combination prediction37
XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data37
Computational methods for the integrative analysis of single-cell data37
PreDTIs: prediction of drug–target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques37
Bioinformatics resources for SARS-CoV-2 discovery and surveillance37
A cross-study analysis of drug response prediction in cancer cell lines37
AVPIden: a new scheme for identification and functional prediction of antiviral peptides based on machine learning approaches37
fastDRH: a webserver to predict and analyze protein–ligand complexes based on molecular docking and MM/PB(GB)SA computation37
GCRFLDA: scoring lncRNA-disease associations using graph convolution matrix completion with conditional random field37
Comprehensive assessment of cellular senescence in the tumor microenvironment36
FireProtASR: A Web Server for Fully Automated Ancestral Sequence Reconstruction36
KGANCDA: predicting circRNA-disease associations based on knowledge graph attention network36
Data science in unveiling COVID-19 pathogenesis and diagnosis: evolutionary origin to drug repurposing36
DeepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding36
RNA–RNA interactions between SARS-CoV-2 and host benefit viral development and evolution during COVID-19 infection36
RNMFLP: Predicting circRNA–disease associations based on robust nonnegative matrix factorization and label propagation36
Bioinformatics and system biology approach to identify the influences of COVID-19 on cardiovascular and hypertensive comorbidities36
Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine36
Immune infiltration and clinical significance analyses of the coagulation-related genes in hepatocellular carcinoma36
Cloud 3D-QSAR: a web tool for the development of quantitative structure–activity relationship models in drug discovery36
Integrative machine learning framework for the identification of cell-specific enhancers from the human genome36
GraphCDR: a graph neural network method with contrastive learning for cancer drug response prediction35
NmRF: identification of multispecies RNA 2’-O-methylation modification sites from RNA sequences35
A new thinking: extended application of genomic selection to screen multiomics data for development of novel hypoxia-immune biomarkers and target therapy of clear cell renal cell carcinoma35
Comprehensive investigation of pathway enrichment methods for functional interpretation of LC–MS global metabolomics data35
AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification35
scCancer: a package for automated processing of single-cell RNA-seq data in cancer35
Critical downstream analysis steps for single-cell RNA sequencing data35
ProtFold-DFG: protein fold recognition by combining Directed Fusion Graph and PageRank algorithm35
Diseasome and comorbidities complexities of SARS-CoV-2 infection with common malignant diseases34
Computationally predicting binding affinity in protein–ligand complexes: free energy-based simulations and machine learning-based scoring functions34
FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction34
MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors34
AniAMPpred: artificial intelligence guided discovery of novel antimicrobial peptides in animal kingdom34
Identifying multi-functional bioactive peptide functions using multi-label deep learning34
Predicting drug–drug interactions by graph convolutional network with multi-kernel34
Prediction of RNA secondary structure including pseudoknots for long sequences33
Spatial transcriptomics prediction from histology jointly through Transformer and graph neural networks33
SGANRDA: semi-supervised generative adversarial networks for predicting circRNA–disease associations33
The Cellular basis of loss of smell in 2019-nCoV-infected individuals33
Identification of biomarkers and pathways for the SARS-CoV-2 infections that make complexities in pulmonary arterial hypertension patients33
DeepFeature: feature selection in nonimage data using convolutional neural network33
Predicting enhancer-promoter interactions by deep learning and matching heuristic33
Topoly: Python package to analyze topology of polymers33
Is acupuncture effective in the treatment of COVID-19 related symptoms? Based on bioinformatics/network topology strategy33
Deep learning in bioinformatics and biomedicine33
Identifying anti-coronavirus peptides by incorporating different negative datasets and imbalanced learning strategies33
Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery33
Depiction of tumor stemlike features and underlying relationships with hazard immune infiltrations based on large prostate cancer cohorts33
A review on longitudinal data analysis with random forest33
Forman persistent Ricci curvature (FPRC)-based machine learning models for protein–ligand binding affinity prediction32
Accurate feature selection improves single-cell RNA-seq cell clustering32